Search Results (2,149)

Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas $ϵ$-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments. Full article

Background: Understanding the duration and quality of immune memory following SARS-CoV-2 infection and vaccination is critical for informing public health strategies and vaccine development. While waning antibody levels have raised concerns about long-term protection, the persistence of memory B cells (MBCs) and T cells plays a vital role in sustaining immunity. Materials and Methods: We conducted a longitudinal prospective study over 12 months, enrolling 285 participants in total, either after natural infection or vaccination with BNT162b2 or mRNA-1273. Peripheral blood samples were collected at four defined time points (baseline, 1–2 months, 6–7 months, and 12–13 months after vaccination or disease onset). Immune responses were assessed through serological assays quantifying anti-RBD IgG and neutralizing antibodies, B-ELISPOT, and multiparameter flow cytometry for S1-specific memory B cells. Results: Both mRNA vaccines induced robust B cell and antibody responses, exceeding those observed after natural infection. Memory B cell frequencies peaked at 6 months and declined by 12 months, but remained above the baseline. The mRNA-1273 vaccine elicited stronger and more durable humoral and memory B-cell-mediated immunity compared to BNT162b2, likely influenced by its higher mRNA dose and longer prime-boost interval. Class-switched memory B cells and S1-specific B cells were significantly expanded in vaccine recipients. Natural infection induced more heterogeneous immune memory. Conclusions: Both mRNA vaccination and natural SARS-CoV-2 infection induce a comparable expansion of memory B cell subsets, reflecting a consistent pattern of humoral immune responses across all studied groups. These findings highlight the importance of vaccination in generating sustained immunological memory and suggest that the vaccine platform and dosage influence the magnitude and durability of immune responses against SARS-CoV-2. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

Objective: To evaluate the effect of a probiotic combination on clinical and oral microbiological parameters in patients with head and neck cancer (HNC) undergoing radiotherapy. Materials and Methods: A randomised, double-blind, placebo-controlled clinical trial was conducted with 72 HNC patients who had received radiotherapy within the past year. Participants were randomly assigned to receive either daily probiotic sachets or placebo for 30 days. Salivary parameters—including unstimulated and stimulated flow rates and pH—were evaluated alongside oral microbiota profiles, including total bacterial load and selected periodontopathogens. Assessments were performed at baseline and post-intervention using sialometry, pH analysis, bacterial culture, and quantitative real-time PCR (qPCR). Results: Sixty-one patients completed the study (31 in the probiotic group, 30 in the placebo group). Stimulated salivary flow increased significantly in the probiotic group (p = 0.0016), while unstimulated flow improved in both groups (p < 0.05). Salivary pH decreased significantly in the probiotic group (p = 0.0209); however, no intergroup differences were observed at the end of the intervention (p = 0.9839). qPCR showed significant reductions in total bacterial load (p = 0.0209) and Fusobacterium nucleatum (p = 0.0080). Culture confirmed the reduction of F. nucleatum (p = 0.0026), with a trend towards significance for total cultivable bacterial count (p = 0.0502). Conclusions: Daily supplementation with a probiotic combination may serve as a practical and well-tolerated adjunctive measure in clinical settings to improve salivary function and reduce key oral pathogens, particularly Fusobacterium nucleatum, in patients undergoing or recovering from radiotherapy for head and neck cancer. These findings support its potential integration into routine supportive care protocols to mitigate xerostomia and oral dysbiosis in this population. Full article

Low-load conditioning activity with blood flow restriction has been addressed as an efficient method to enhance an individual’s performance during their main exercise activity. However, the optimal degree of blood flow restriction remains unclear. Therefore, this study investigated the acute effects of low-load conditioning activity with different degrees of blood flow restriction on muscle strength, power, and perceived exertion. Twenty recreationally trained men (20.9 ± 2.3 years) participated in a randomized crossover design including three conditions: control, low-load blood flow restriction at 50%, and 75% of total arterial occlusion pressure. Participants performed squats (three sets of ten reps) followed by isokinetic assessments of the knee flexor and extensor performance at 7 and 10-min post-exercise. The session rating of perceived exertion (SRPE) was recorded 30 min after each session. No significant effects were observed for condition, time, or their interaction on peak torque, total work, or average power (p < 0.05). However, SRPE was significantly higher in the 75% BFR condition compared to both the 50% BFR and control conditions (p < 0.05), with no difference between the 50% BFR and control. These findings suggest that low-load conditioning activity with blood flow restriction does not acutely enhance neuromuscular performance. However, a higher degree of restriction increases perceived exertion. Full article

Accurate calibration of mesoscopic contact model parameters is essential for ensuring the reliability of Particle Flow Code in Three Dimensions (PFC3D) simulations in geotechnical engineering. Trial-and-error approaches are often used to determine the parameters of the contact model, but they are time-consuming, labor-intensive, and offer no guarantee of parameter validity or simulation credibility. Although conventional machine learning techniques have been applied to invert the contact model parameters, they are hampered by the difficulty of selecting the optimal hyperparameters and, in some cases, insufficient data, which limits both the predictive accuracy and robustness. In this study, a total of 361 PFC3D uniaxial compression simulations using a linear parallel bond model with varied mesoscopic parameters were generated to capture a wide range of rock and geotechnical material behaviors. From each stress–strain curve, eight characteristic points were extracted as inputs to a multi-objective Automated Machine Learning (AutoML) model designed to invert three key mesoscopic parameters, i.e., the elastic modulus (E), stiffness ratio (k_s/k_n), and degraded elastic modulus (E_d). The developed AutoML model, comprising two hidden layers of 256 and 32 neurons with ReLU activation function, achieved coefficients of determination (R²) of 0.992, 0.710, and 0.521 for E, k_s/k_n, and E_d, respectively, demonstrating acceptable predictive accuracy and generalizability. The multi-objective AutoML model was also applied to invert the parameters from three independent uniaxial compression tests on rock-like materials to validate its practical performance. The close match between the experimental and numerically simulated stress–strain curves confirmed the model’s reliability for mesoscopic parameter inversion in PFC3D. Full article

The electrodynamics of current provide much of our technology, from telegraphs to the wired infrastructure powering the circuits of our electronic technology. Current flow is analyzed by its own rules that involve the Maxwell Ampere law and magnetism. Electrostatics does not involve magnetism, and so current flow and electrodynamics cannot be derived from electrostatics. Practical considerations also prevent current flow from being analyzed one charge at a time. There are too many charges, and far too many interactions to allow computation. Current flow is essential in biology. Currents are carried by electrons in mitochondria in an electron transport chain. Currents are carried by ions in nerve and muscle cells. Currents everywhere follow the rules of current flow: Kirchhoff’s current law and its generalizations. The importance of electron and proton flows in generating ATP was discovered long ago but they were not analyzed as electrical currents. The flow of protons and transport of electrons form circuits that must be analyzed by Kirchhoff’s law. A chemiosmotic theory that ignores the laws of current flow is incorrect physics. Circuit analysis is easily applied to short systems like mitochondria that have just one internal electrical potential in the form of the Hodgkin Huxley Katz (HHK) equation. The HHK equation combined with classical descriptions of chemical reactions forms a computable model of cytochrome c oxidase, part of the electron transport chain. The proton motive force is included as just one of the components of the total electrochemical potential. Circuit analysis includes its role just as it includes the role of any other ionic current. Current laws are now needed to analyze the flow of electrons and protons, as they generate ATP in mitochondria and chloroplasts. Chemiosmotic theory must be replaced by an electro-osmotic theory of ATP production that conforms to the Maxwell Ampere equation of electrodynamics while including proton movement and the proton motive force. Full article

Xerostomia, the subjective complaint of a dry mouth, is frequently associated with salivary flow reduction and/or salivary gland hypofunction. This condition significantly impacts an individual’s quality of life and oral health, including difficulties in speaking, chewing, and swallowing. Xerostomia may be caused by autoimmune diseases, xerogenic medications, and radiation therapy. Our objective was to identify differentially expressed proteins in the saliva of patients with medication and autoimmune disease-associated xerostomia compared to non-xerostomic control subjects. Two groups of individuals (N = 45 total) were recruited: non-xerostomic subjects (NX-group; n = 18) and xerostomic patients (XP-group; n = 27). Dried saliva spot samples were collected from major salivary glands, i.e., parotid (left and right) and submandibular glands. Proteomic analysis was performed by deep nanoLC-MS/MS. Differential protein expression in the XP-group relative to the NX-group was determined by the Mann–Whitney U-test with FDR Benjamini–Hochberg correction (p_adj < 0.05). The Search Tool for Recurring Instances of Neighboring Genes (STRING_v12.0) was used to generate interaction networks and perform pathway analysis. A total of 1407 proteins were detected. Of these, 86 from the left parotid gland, 112 from the right parotid gland, and 73 from the submandibular gland were differentially expressed proteins (DEPs). Using STRING analysis, we identified, for the first time, several neurodegenerative disease-associated networks, primarily involving the downregulation of the 20S proteasome core complex and glyoxalase proteins across salivary glands. In this study, we determined neuronal dysregulation and impaired methylglyoxal (MGO) detoxification, possibly through reduced protein expression of glyoxalase Parkinson’s Disease (PD) Protein 7 (encoded by the PARK7 gene) in major salivary glands of xerostomic patients. Indeed, impaired MGO detoxification has been previously shown to cause salivary gland dysfunction in a mouse model of type 2 diabetes. Based on other DEPs associated with neurodegenerative disorders, our results also suggest a possible deficiency in the parasympathetic nervous system innervation of salivary glands, warranting further investigation. Full article

A green scheduling problem is proposed in this work, where both constraints on intermediate storage capacity and job transportation requirements are simultaneously considered. An improved discrete pathfinder algorithm (IDPFA) with multi-neighborhood local search is proposed to minimize the maximum completion time and total energy consumption. The algorithm addresses the green flow shop scheduling problem with limited buffers and automated guided vehicle (GFSSP_LBAGV). Firstly, based on the machine speed constraints, the transportation time for moving jobs by the automated guided vehicle (AGV) is incorporated to establish a mathematical model. Secondly, the core idea of the pathfinder algorithm (PFA) is applied to the evolutionary process of the discrete PFA, where three different crossover operations are used to replace the exploration process of the pathfinder, the influence of the pathfinder on the followers, and the mutual learning among the followers. Then, a multi-neighborhood local search is employed to conduct a detailed exploration of high-quality solution spaces. Finally, extensive standard test sets are used to verify the effectiveness of the proposed IDPFA in solving GFSSP_LBAGV. Full article

This paper examines the operational consequences and energy demands associated with the growing cultural diversity of air travellers at airport security checkpoints. The analysis focuses on how an increasing proportion of passengers requiring enhanced security screening, due to cultural, religious, or linguistic factors, affects both system throughput and energy consumption. The methodology integrates synchronised measurement of passenger flow with real-time monitoring of electricity usage. Four operational scenarios, representing incremental shares (0–15%) of passengers subject to extended screening, were modelled. The findings indicate that a 15% increase in this passenger group leads to a statistically significant rise in average power consumption per device (3.5%), a total energy usage increase exceeding 4%, and an extension of average service time by 0.6%—the cumulative effect results in a substantial annual contribution to the airport’s carbon footprint. The results also reveal a higher frequency and intensity of power consumption peaks, emphasising the need for advanced infrastructure management. The study emphasises the significance of predictive analytics, dynamic resource allocation, and the implementation of energy-efficient technologies. Furthermore, systematic intercultural competency training is recommended for security staff. These insights provide a scientific basis for optimising airport security operations amid increasing passenger heterogeneity. Full article

With the continuous expansion and increasing operational complexity of high-speed railway networks, there is a growing need for more flexible and efficient EMU (Electric Multiple Unit) routing strategies. To address these challenges, in this paper, we propose a multi-day flexible circulation model that minimizes total connection time and deadheading mileage. A multi-commodity network flow model is formulated, incorporating constraints such as first-level maintenance intervals, storage capacity, train coupling/decoupling operations, and train types, with across-day consistency. To solve this complex model efficiently, a heuristic decomposition algorithm is designed to separate the problem into daily service chain generation and EMU assignment. A real-world case study in the Beijing–Baotou high-speed corridor demonstrates the effectiveness of the proposed approach. Compared to a fixed strategy, the flexible strategy reduces EMU usage by one unit, lowers deadheading mileage by up to 16.4%, and improves maintenance workload balance. These results highlight the practical value of flexible EMU deployment for large-scale, multi-day railway operations. Full article

This paper proposes a novel integer-mixed probabilistic optimal power flow (IM-POPF) strategy for frequency regulation in islanded microgrids under uncertain operating conditions. Existing load-shedding approaches face critical limitations: continuous frameworks fail to reflect the discrete nature of actual load disconnections, while deterministic models inadequately capture the stochastic behavior of renewable generation and load variations. The proposed approach formulates load shedding as an integer optimization problem where variables are categorized as integer (load disconnection decisions at specific nodes) and continuous (voltages, power generation, and steady-state frequency), better reflecting practical power system operations. The key innovation combines integer load-shedding optimization with efficient uncertainty propagation through Unscented Transformation, eliminating the computational burden of Monte Carlo simulations while maintaining accuracy. Load and renewable uncertainties are modeled as normally distributed variables, and probabilistic constraints ensure operational limits compliance with predefined confidence levels. The methodology integrates Differential Evolution metaheuristics with Unscented Transformation for uncertainty propagation, requiring only 137 deterministic evaluations compared to 5000 for Monte Carlo methods. Validation on an IEEE 33-bus radial distribution system configured as an islanded microgrid demonstrates significant advantages over conventional approaches. Results show 36.5-fold computational efficiency improvement while achieving 95.28% confidence level compliance for frequency limits, compared to only 50% for deterministic methods. The integer formulation requires minimal additional load shedding (21.265%) compared to continuous approaches (20.682%), while better aligning with the discrete nature of real-world operational decisions. The proposed IM-POPF framework successfully minimizes total load shedding while maintaining frequency stability under uncertain conditions, providing a computationally efficient solution for real-time microgrid operation. Full article

This study presents a microfluidic chip platform designed using a multiscale 3D printing strategy for fabricating microfluidic chips with integrated, high-resolution, and customizable membrane structures. By combining two-photon polymerization (2PP) for submicron membrane fabrication with liquid crystal display printing for rapid production of larger components, this approach addresses key challenges in membrane integration, including sealing reliability and the use of transparent materials. Compared to fully 2PP-based fabrication, the multiscale method achieved a 56-fold reduction in production time, reducing total fabrication time to approximately 7.2 h per chip and offering a highly efficient solution for integrating complex structures into fluidic chips. The fabricated chips demonstrated excellent mechanical integrity. Burst pressure testing showed that all samples withstood internal pressures averaging 1.27 ± 0.099 MPa, with some reaching up to 1.4 MPa. Flow testing from ~35 $\mathsf{\mu }$L/min to ~345 $\mathsf{\mu }$L/min confirmed stable operation in 75 $\mathsf{\mu }$m square channels, with no leakage and minimal flow resistance up to ~175 $\mathsf{\mu }$L/min without deviation from the predicted behavior in the 75 $\mathsf{\mu }$m. Membrane-integrated chips exhibited outlet flow asymmetries greater than 10%, indicating active fluid transfer across the membrane and highlighting flow-dependent permeability. Overall, this multiscale 3D printing approach offers a scalable and versatile solution for microfluidic device manufacturing. The method’s ability to integrate precise membrane structures enable advanced functionalities such as diffusion-driven particle sorting and molecular filtration, supporting a wide range of biomedical, environmental, and industrial lab-on-a-chip applications. Full article

In this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO₂ emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent in the operation of such networks in an environment with D-STATCOMs. To solve such a problem, we used three master–slave methodologies based on sequential programming methods. In the proposed methodologies, the master stage solves the problem of intelligent D-STATCOM operation using the continuous versions of the Monte Carlo (MC) method, the population-based genetic algorithm (PGA), and the Particle Swarm Optimizer (PSO). The slave stage, for its part, evaluates the solutions proposed by the algorithms to determine their impact on the objective functions and constraints representing the problem. This is accomplished by running an Hourly Power Flow (HPF) based on the method of successive approximations. As test scenarios, we employed the 33- and 69-node radial test systems, considering data on power demand and CO₂ emissions reported for the city of Medellín in Colombia (as documented in the literature). Furthermore, a test system was adapted in this work to the demand characteristics of a feeder located in the city of Talca in Chile. This adaptation involved adjusting the conductors and voltage limits to include a test system with variations in power demand due to seasonal changes throughout the year (spring, winter, autumn, and summer). Demand curves were obtained by analyzing data reported by the local network operator, i.e., Compañía General de Electricidad. To assess the robustness and performance of the proposed optimization approach, each scenario was simulated 100 times. The evaluation metrics included average solution quality, standard deviation, and repeatability. Across all scenarios, the PGA consistently outperformed the other methods tested. Specifically, in the 33-node system, the PGA achieved a 24.646% reduction in energy losses and a 0.9109% reduction in CO₂ emissions compared to the base case. In the 69-node system, reductions reached 26.0823% in energy losses and 0.9784% in CO₂ emissions compared to the base case. Notably, in the case of the Talca feeder—particularly during summer, the most demanding season—the PGA yielded the most significant improvements, reducing energy losses by 33.4902% and CO₂ emissions by 1.2805%. Additionally, an uncertainty analysis was conducted to validate the effectiveness and robustness of the proposed optimization methodology under realistic operating variability. A total of 100 randomized demand profiles for both active and reactive power were evaluated. The results demonstrated the scalability and consistent performance of the proposed strategy, confirming its effectiveness under diverse and practical operating conditions. Full article

The air injection for brine drainage affects the thermodynamic characteristics of salt caverns in the operation of compressed air energy storage (CAES). This study develops a thermodynamic model to predict temperature and pressure variations during brine drainage and operational cycles, validated against Huntorf plant data. Results demonstrate that increasing the air injection flow rate from 80 to 120 kg/s reduces the brine drainage initiation time by up to 47.3% and lowers the terminal brine drainage pressure by 0.62 MPa, while raising the maximum air temperature by 4.9 K. Similarly, expanding the brine drainage pipeline cross-sectional area from 2.99 m² to 9.57 m² reduces the total drainage time by 33.7%. Crucially, these parameters determine the initial pressure and temperature at the completion of brine drainage, which subsequently shape the pressure bounds of the operational cycles, with variations reaching 691.5 kPa, and the peak temperature fluctuations, with differences of up to 4.9 K during the first cycle. This research offers insights into optimizing the design and operation of the CAES system with salt cavern air storage. Full article